Adaptive Patient Population Selection Design in Clinical Trials

Abstract

For the success of a new drug development, it is crucial to select the sensitive patient populations. To potentially reduce timeline and cost, we may apply a two-stage adaptive patient population selection design to a therapeutic trial. In such a design, based on early results of the trial, patient population(s) will be selected/determined for the final stage and analysis. Because of this adaptive nature and the multiple between-treatment comparisons for multiple populations, an alpha adjustment is necessary. In this article, we propose a closed step-down testing procedure to assess treatment effects on multiple populations and a weighted combination test to combine data from the two stages after sample size adaptation. Computation/simulation is used to compare the performances of the proposed procedure and the other multiplicity adjustment procedures. A trial simulation is presented to illustrate the application of the methods.

Key Words:

• Closed step-down testing procedure
• Conditional power
• Sample size re-estimation
• Trial simulation
• Weighted combination test

Acknowledgment

We thank Loic Darchy, Nathalie Fretault, the two referees, and an associate editor for their helpful comments on the initial draft of this manuscript.